Last night during the heart-stopping Syracuse-Wisconsin game, Harry and I were talking (okay, Harry was mostly watching his team win by the skin of its teeth) about ways to improve the Brooks Baseball player card system. We exchanged some data and are presenting the first of our “data-driven” search tools—pitcher similarity.
This feature is incredibly beta and likely to change over the next few weeks, but right now when you search for a player (let’s pick Josh Beckett), you will get a table listing other players in the “Josh Beckett Family,” along with the “distance” to each player.
The scores are generated by comparing a vector of pitch speed, frequency, release, spin angle, and spin rate using MATLAB’s knnsearch algorithm to identify neighbors. Currently, we’re presenting the top five neighbors for each pitcher.
These are not perfect right now. We haven’t weighted the scores yet (that’s another conversation over basketball), so while we do a good job representing pitch mix and style, we’re not doing a very good capturing pitch speed yet.
Punch in a few pitchers, and let us know how our system is doing. Let us know over Twitter if we’ve really missed on someone. I’m @brooksbaseball, and Harry is @harrypav.